U.S. patent application number 13/458792 was filed with the patent office on 2012-08-23 for predicting dynamic transportation demand with mobility data.
This patent application is currently assigned to International Business Machines Corporation. Invention is credited to Wei Xiong Shang, YiBo Zhang, Jin Zhou, Yan Feng Zhu.
Application Number | 20120215586 13/458792 |
Document ID | / |
Family ID | 46529097 |
Filed Date | 2012-08-23 |
United States Patent
Application |
20120215586 |
Kind Code |
A1 |
Shang; Wei Xiong ; et
al. |
August 23, 2012 |
PREDICTING DYNAMIC TRANSPORTATION DEMAND WITH MOBILITY DATA
Abstract
A travel information server estimates travel demand with
mobility data. The server identifies activity types of users based,
at least in part, on mobility data of the users. The mobility data
has been collected over time and indicates at least locations and
corresponding times at the locations. Travel information is
generated with the mobility data for each of the activity types.
The travel information for a first of the activity types is
adjusted based, at least in part, on travel-related event data that
corresponds to the first activity type to generate an adjusted
travel information for the first activity type. The travel-related
event data indicates an event that potentially influences travel
for a short term computing an estimated travel demand with a
combination of the adjusted travel information for the first
activity type and the travel information for at least a second of
the activity types.
Inventors: |
Shang; Wei Xiong; (Beijing,
CN) ; Zhang; YiBo; (Beijing, CN) ; Zhou;
Jin; (Beijing, CN) ; Zhu; Yan Feng; (Beijing,
CN) |
Assignee: |
International Business Machines
Corporation
Armonk
NY
|
Family ID: |
46529097 |
Appl. No.: |
13/458792 |
Filed: |
April 27, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13332245 |
Dec 20, 2011 |
|
|
|
13458792 |
|
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06Q 30/0202 20130101;
H04W 4/029 20180201 |
Class at
Publication: |
705/7.31 |
International
Class: |
G06Q 30/02 20120101
G06Q030/02 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 24, 2011 |
CN |
201110027185.1 |
Claims
1. A method comprising: identifying activity types of users based,
at least in part, on mobility data of the users, wherein the
mobility data has been collected over time and indicates at least
locations and corresponding times at the locations; generating
travel information with the mobility data for each of the activity
types; adjusting the travel information for a first of the activity
types based, at least in part, on travel-related event data that
corresponds to the first activity type to generate an adjusted
travel information for the first activity type, wherein the
travel-related event data indicates an event that potentially
influences travel for a short term; computing an estimated travel
demand with a combination of the adjusted travel information for
the first activity type and the travel information for at least a
second of the activity types.
2. The method of claim 1, wherein the mobility data comprises at
least one of call data, short message service data, web page
browsing data, public transportation transaction data, location
area handover control data, and packet switched data.
3. The method of claim 1 further comprising collecting the mobility
data from a plurality of mobile switching centers over a period of
time.
4. The method of claim 1, wherein the event comprises one of a
transportation regulation, a change in traffic flow, a chance in
work hours, a change in school hours, a traffic accident, and a
construction event.
5. The method of claim 1, wherein said identifying activity types
of users, based at least in part, on mobility data of users
comprises: determining durations of the users within different cell
locations with the mobility data; determining geographic locations
that correspond to the different cell locations based, at least in
part, on established correspondence between the different cell
locations and respective ones of the geographic locations; and
identifying the activity types based on the durations and the
geographic locations.
6. The method of claim 5 further comprising establishing a
correspondence between each of the different cell locations in one
or more mobile communication networks and corresponding ones of the
geographic locations determined with a geographical information
system.
7. The method of claim 5, wherein said identifying the activity
types is also based on beginning and ending times of the durations
at the different cell locations.
8. The method of claim 1 further comprising determining origins and
destinations for each of the users based, at least in part, on the
mobility data.
9. The method of claim 8, wherein said determining origins and
destinations for each of the users based, at least in part, on the
mobility data comprises: computing an approximate average moving
speed for the user within given periods of time; classifying those
of the locations that precede a transition from a prolonged lack of
movement to a greater moving speed as origins at corresponding
times; classifying those of the locations that correspond with
prolonged lack of movement as destinations at corresponding
times.
10. The method of claim 8 further comprising classifying those of
the locations that correspond to a low moving speed for short
periods of time as middle stop locations.
11. The method of claim 8, wherein said generating the travel
information with the mobility data for each of the activity types
comprises: generating a matrix for each of the identified activity
types, wherein the matrix indicates a number of users that travel
between each pair of the origins and the destinations for the
activity type.
Description
RELATED APPLICATIONS
[0001] This continuation application claims the benefit under 35
U.S.C. .sctn.120 of U.S. patent application Ser. No. 13/332,245
filed Dec. 20, 2011. U.S. patent application Ser. No. 13/332,245
claims benefit under 35 U.S.C. .sctn.119 of Chinese Application No.
201110027185.1, which was filed Jan. 24, 2011.
BACKGROUND
[0002] The present inventive subject matter relates to a demand
data acquisition technology, and more particularly, to a method and
an apparatus for providing travel information.
[0003] Transportation demand data are crucial to urban
transportation planning (such as road planning, subway planning,
etc.) as well as transportation facility configuration.
Traditionally, transportation demand data acquisition is mainly
conducted through paper survey on citizens. Paper survey not only
consumes labor and financial resources, but also takes a rather
long time to obtain data. Moreover, the data obtained from such a
survey are generally static and long-term statistics, which
therefore can only be applied to handle long-term issues such as
planning and development.
[0004] Data acquired in such a way lag behind the current
transportation demand and cannot be suitable for various changes.
Thus, various kinds of planning, provisions and measures that are
made based on these data usually cannot achieve the expected
objectives.
[0005] Hence, a desire for "dynamic transportation demand," which
means a time varying traffic flow, has become more and more urgent.
Dynamic transportation demand is generally influenced by
transportation facility and behaviors of people. Dynamic
transportation demand is basic information for fine tuning
transportation facilities, traffic lights, and short-term
transportation policies. While a temporal event occurs, a
de-congestion scheme may be designed also based on such
information.
[0006] It is known that in many countries and regions, the coverage
of mobile communication networks has reached at least 90%, and
mobile communication devices have become increasingly prevalent.
Further, mobile networks can record a user's positions based on
cell-towers, which provides a possibility of obtaining a sojourn of
people at a specific location. Thus, transportation demand data may
be acquired based on the mobile network. Its basic principle is to
obtain main positions of people within predetermined regions, for
example, "home," "office," "school," "shopping region," etc., and
obtain potential behaviors of the people from the mobility data
based on these positions.
SUMMARY
[0007] Embodiments of the inventive subject matter include a method
for estimating travel demand with mobility data. The method
identifies activity types of users based, at least in part, on
mobility data of the users. The mobility data has been collected
over time and indicates at least locations and corresponding times
at the locations. Travel information is generated with the mobility
data for each of the activity types. The travel information for a
first of the activity types is adjusted based, at least in part, on
travel-related event data that corresponds to the first activity
type to generate an adjusted travel information for the first
activity type. The travel-related event data indicates an event
that potentially influences travel for a short term computing an
estimated travel demand with a combination of the adjusted travel
information for the first activity type and the travel information
for at least a second of the activity types.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The present embodiments may be better understood, and
numerous objects, features, and advantages made apparent to those
skilled in the art by referencing the accompanying drawings.
[0009] FIG. 1 schematically illustrates a diagram of correspondence
relationships between cells of a mobile network and actual physical
positions.
[0010] FIG. 2 schematically illustrates an exemplary structure of
devices in a mobile network.
[0011] FIG. 3 illustrates a flow chart of a method for providing
travel information according to an embodiment of the present
invention.
[0012] FIG. 4A schematically illustrates a diagram of identifying
meaningful locations.
[0013] FIG. 4B schematically illustrates a diagram of identifying
sojourn locations.
[0014] FIG. 4C schematically illustrates a diagram of a method of
identifying a travel mode.
[0015] FIG. 4D schematically illustrates a diagram of another
method of identifying the travel mode.
[0016] FIG. 4E schematically illustrates a diagram of a further
method of identifying the travel mode.
[0017] FIGS. 5A and 5B schematically illustrate a diagram of an O-D
matrix.
[0018] FIG. 6 illustrates a flowchart of a method of providing
travel information according to another embodiment of the present
invention.
[0019] FIG. 7A and FIG. 7C schematically illustrate diagrams of
travel conditions of three kinds of activity types.
[0020] FIG. 7D schematically illustrates the adjusted travel
conditions of the activity types as illustrated in FIG. 7C.
[0021] FIG. 8 schematically illustrates a system architecture of an
application environment of a method according to the present
invention.
[0022] FIG. 9 schematically illustrates a block diagram of an
apparatus for providing travel information according to the present
invention.
[0023] FIG. 10 schematically illustrates a structural block diagram
of a computer device in which embodiments of the present invention
can be implemented.
DESCRIPTION OF EMBODIMENT(S)
[0024] The description that follows includes exemplary systems,
methods, techniques, instruction sequences and computer program
products that embody techniques of the present inventive subject
matter. However, it is understood that the described embodiments
may be practiced without these specific details. In other
instances, well-known instruction instances, protocols, structures
and techniques have not been shown in detail in order not to
obfuscate the description.
[0025] The term "activity" refers to the moving behavior of people
with a particular purpose. "Moving behavior" may be represented by
a sequence containing time and locations, which reflects when a
user starts from which place to which destination, and stops at
which places in between; and it includes origination, destination,
sojourn locations, and corresponding time. The term "sojourn"
refers to a temporary stop or location of a temporary stop. The
location of a temporary stop is also referred to as a sojourn
location. Although being at home for 10 hours a day is also
temporary with respect to an entire day or longer period of time,
this description considers stops of a longer duration, such as
being at home or work, a more stable stop and not a temporary stop
or temporary location. The "particular purpose" of a moving
behavior refers to the reason of a moving behavior, for example,
going to work, shopping, taking children to school, picking up
children from school, going to work plus taking children to school,
etc. The term "nature of location" refers to the corresponding
meaning of the location, such as school, shopping place, etc. The
term "moving mode" refers to the mode by which the user performs
the moving behavior, for example, by bike, by a private car, by
bus, etc. The term "mobility data" refers to data indicating
location information and corresponding time of the user, which may
come from the mobile communication network, where the location
information may be for example a cell ID. The term "travel-related
event" refers to an event that potentially influences travel of
people temporally, in a short time, or in a short term; it
comprises events such as short time traffic flow restriction or
temporal transportation regulation, as well as some transportation
regulation measures or regulations, such as "adjusted work hours,"
etc.
[0026] A mobile communication network from which mobility data may
be obtained will be briefly described with reference to FIG. 1 and
FIG. 2, wherein FIG. 1 schematically illustrates a diagram of
correspondence relationships between cells of a mobile network and
actual physical locations; and FIG. 2 schematically illustrates an
exemplary structure of a mobile network system.
[0027] As illustrated in FIG. 1, a mobile communication network
generally comprises several cellular cells that substantially cover
respective areas. In many countries and regions, the coverage rate
has amounted to more than 90%. As illustrated in the figure, areas
in the map have been illustrated as having been completely covered
by a plurality of corresponding cells represented by hexagons. Each
cell may be mapped to an actual physical location. Various
approaches may be employed to establish a mapping or corresponding
relationship between cells of the mobile network and physical
locations. For example, these correspondence relationships may be
established by exploiting information such as geographical location
data in a geographical information system, and cell locations in
the mobile communication network.
[0028] Reference is made to FIG. 2, which schematically illustrates
an exemplary structure of a mobile network system. As illustrated
in FIG. 2, in the mobile communication network, there is generally
provided a base station (BS) in each cell, and mobility data of a
mobile user will be transited to a mobile switch center through the
BS in a corresponding cell. Each mobile switching center (MSC)
generally manages several cells and is responsible for managing the
function of call connection, handover control, radio channel
management, and etc. In each MSC, there is included a home location
register (HLR), a visitor location register (VLR), and an
authentication center. The HLR is responsible for a data region of
the mobile user management, and stores user home information and
the current location information. The VLR serves mobile users
within its control area and stores information on a registered
mobile user that has entered into its managed area. Once the mobile
user leaves the controlled area of the VLR, then re-registration
will be made in another VLR that manages the area into which he
enters, while the original VLR will no longer record the mobile
user data. The AUC is for implementing functions such as user
authentication. Thus, it is very clear that mobility data
containing locations (particularly cell locations) and time may be
obtained in the mobile switch center.
[0029] Next, a plurality of embodiments of a method for providing
traveling information according to the present inventive subject
matter will be described with reference to FIGS. 3 to 8. First,
with reference to FIG. 3, FIG. 3 illustrates a flowchart of a
method for providing travel information according to one embodiment
of the present inventive subject matter.
[0030] As illustrated in FIG. 3, first, at step 301, activity types
of users are identified from mobility data containing time and
location information.
[0031] From the description with reference to FIG. 2, it is seen
that the mobility data containing time and location information can
be obtained from the mobile communication network, particularly
from the mobile switch center. Generally, the MSC will record the
location of the user and the corresponding time in the process of
using a mobile device, for example, during calling, sending and
receiving an SMS, or browsing webpage data, or performing packet
switched (data downloading and data uploading). Therefore, call
data, SMS data, webpage browsing data, and packet switched data may
all be used as mobility data employed in the present inventive
subject matter. Further, it is also known that, when the mobile
device is in a standby mode, location area handover data will be
generated in case of crossing a location area during the moving
process of the user, and such location area handover data likewise
contain time and location information. Therefore, location area
handover data may also be employed in the present inventive subject
matter.
[0032] Additionally, a "public transportation card integrated cell
phone" has arisen, where the cell phone may be directly used to
perform card-swiping charging when taking a bus or a taxi. Such
data, generally also carrying data like payment time and location
of the mobile device, can also be used in the present inventive
subject matter. Further, these inventors also notice that with the
prevalence of public transportation cards, account transaction data
for the public transportation cards will also carry information
like transaction time, sites of getting on/off, and time. Such
information may likewise reflect the moving state of the user, and
is thus used in the present inventive subject matter.
[0033] Daily moving behaviors of users may be determined based on
these mobility data. The moving behavior refers to a sequence of
information containing time and locations, which reflects when the
user is at which location. Then, the purpose for the moving
behaviors can be determined based on the nature and/or time of
these locations. Next, the activity types can be determined based
on the moving behaviors and behavior targets thereof Regarding the
process of these operations, description will be made in detail
hereinafter.
[0034] It should be noted that one day of moving data is obtained
in order to reflect the daily activities of each user. However, in
order to obtain more accurate daily activity information, mobility
data for more days may be employed, for example the mobility data
for one month, three months, half a year, or within any suitable
period of time.
[0035] Next, embodiments of determining various activity types
based on mobility data will be described in detail with reference
to FIGS. 4A to 4E.
[0036] Reference is made to FIG. 4A, which schematically
illustrates a diagram of a method of identifying meaningful
locations. Specifically, FIG. 4A illustrates time and location
information indicated by mobility data of a user, where the X axis
represents time in one day and the Y axis represents locations
indicated by Cell IDs. As illustrated in FIG. 4A, the user sojourns
within cells with IDs of 6 and 4 for a long time between 0:00 and
8:00 am; from 9:00 am to 13:00 pm and from 15:00 pm to 19:00 pm,
the user substantially sojourns within cells with Cell IDs of 2 and
3; and afterwards till 21:00 pm, the user is substantially located
within cells with Cell IDs of 6 and 4. In addition, there are some
moving behaviors between 8:00 am and 9:00 am, between 13:00 pm and
14:30 pm, and between 19:00 pm and 20:00 pm.
[0037] Based on the data in FIG. 4A, it can be determined that Cell
6 and Cell 4 are locations for long-term sojourn, so are Cell 2 and
Cell 3. These locations are originations and destinations of major
daily moving behaviors of the user. The originations and
destinations may be determined based on the feature of the data
themselves. For example, for the moving behavior between
approximately 8:00 am and 9:00 am as illustrated in the figure, the
user starts activity from Cell 6 and Cell 4 in which they have been
static for a long time, and sojourns within Cell 2 and Cell 3 after
arriving at Cell 2 and Cell 3, which means for the moving behavior
between 8:00 am and 9:00 am, Cell 6 and Cell 4 are origination,
while Cell 3 and Cell 2 are destination. Therefore, for example for
the moving behavior between 8:00 am and 9:00 am, the following
moving behavior may be obtained: Cell 6&4: 8:00 am; Cell
3&2: 9:00 am.
[0038] According to the scenario as illustrated in FIG. 4A, it may
be seen that the user sojourns for a long term within Cell IDs 6
and 4 between 0:00 am and 8:00 am; the user substantially sojourns
within Cell IDs 2 and 3 between 9:00 am and 13:00 pm and between
15:00 pm and 19:00 pm; afterwards, after 9:00 am, the user is
substantially located within Cell 6 and Cell 4. Thus, it may be
determined based on the time information that the Cells 6&4
correspond to the home of the user, while Cells 3&2 correspond
to the office of the user. In this example, two cells correspond to
one location because the coverage of respective cells usually
overlaps in a mobile communication network. Hence, even if the user
does not move, the user's location may correspond to two cells due
to such overlap. In other words, a single physical location may
correspond to mobility data that indicates 2 cells that
overlap.
[0039] It may be further determined whether a moving behavior has a
middle stop. A middle stop will be described with reference to FIG.
4B, which illustrates a diagram of a method of determining a middle
stop. An "entropy" distribution of the user within 24 hours a day
is obtained based on the mobility data of the user.
[0040] The term "entropy," also called "transient entropy," is a
variant corresponding to the moving speed of the user. The value of
the entropy may be calculated in the following manner: determining
a probability P, for a user sojourning in each cell i (i=1, . . .
n) during a predetermined period of time (for example, 3 minutes, 5
minutes, or any suitable period of time selected based on an
application), for example, the percentage of time the user sojourns
in each cell i within a given period of time to a given length of a
period of time); obtaining a dimensionless variant based on the
probability P.sub.i: P.sub.i Log.sub.2 P.sub.i; then summing and
negating the dimensionless variant to obtain the following
equation:
E = - i = 1 n P i Log 2 P i Equation ( 1 ) ##EQU00001##
[0041] The E just corresponds to the above term "entropy." In the
same cell condition, a larger user entropy indicates a higher
moving speed of the user within the period of time; and a smaller
entropy indicates a lower moving speed of the user within the
period of time. Thus, the entropy may be a measurement on
speed.
[0042] For example, as illustrated in FIG. 4B, it is clear that
during the moving behavior between 7:00 am and 9:00 am, the user
starts activity at around 7:00 am. However, during this moving
period, entropy of the user decreases to a smaller value and then
rises again. Based on this feature, it may be determined that the
point corresponding to the smaller value is a sojourning site. As
illustrated in FIG. 4B, the point as indicated by the arrows
corresponds to middle sojourning sites.
[0043] It should be noted that such meaningful sojourning sites are
different from other temporal/brief stops. For example, temporal
stops caused by factors such as traffic lights are random and more
brief than middle sojourning sites, while the middle sojourning
sites in the sense of this inventive subject matter regularly
appear daily and have a stronger regular nature. On this basis,
middle sojourning sites or locations (also referred to as
"meaningful sojourning sites")may be distinguished from other
temporal stops (also referred to as "brief stops" or "brief
temporal sites").
[0044] In this way, locations that are meaningful to each user and
their corresponding time may be obtained to thereby determine the
daily moving behavior of the user. Such information is statistical
information obtained based on long-term mobility data of the user,
which more accurately reflects the daily behaviors of the user.
[0045] The target for the moving behavior may be determined based
on the nature and/or time as contained in the moving behavior. For
some locations such as "home" and "office" as above mentioned,
their natures may be determined based on time.
[0046] However, for some locations, it would be difficult to
determine their meanings or functions merely based on time
information. In an embodiment of the present inventive subject
matter, meanings of locations may be identified using an existing
geographical information system. For example, some locations, such
as a school, a large shopping mall, a hospital, etc., may be
selected from the geographical information system so as to match
geographical coordinates of these locations with geographical
locations of the cells thereby determining the meanings of
corresponding locations. In this way, the purpose of a moving
behavior may be determined. For example, it may be determined that
the purpose is "shopping" based on the destination being a shopping
mall. Further, the travel target may be determined by combining the
time information of a location and the meaning of the location.
[0047] After obtaining the moving behavior and its purpose, the
activity type of the user may be determined, for example, going to
work, coming back from work, taking children to school, picking up
children from school, going to work plus taking children to school,
etc. These activity types may be given suitable names or are only
characterized by data information containing location, time, and
behavior target.
[0048] Through the above mentioned manners, the mobility data for
all users may be analyzed to obtain various activity types of the
users.
[0049] Next, at step 302 as illustrated in FIG. 3, travel
information with respect to at least one of the activity types may
be formed using the mobility data.
[0050] In particular, a corresponding origination-destination
matrix O-D may be first constructed for each activity type.
However, it should be noted that the travel information according
to the present inventive subject matter is not so limited, but may
be in any suitable manner.
[0051] FIG. 5A schematically illustrates an example of an O-D
matrix structure, wherein the elements "1," "2," "3," and "4" in
the first row represent the Cell IDs at the locations of
originations, while the elements "1," "2," "3," and "4" in the
first column represent the Cell IDs at the locations of
destinations. As illustrated in the figure, upon initially
constructing, the element "x" at intersections of rows and columns
with identical Cell IDs represents the case of making no statistics
or not existing; other data elements in the matrix are 0, which
indicate the number of people starting from the area identified by
a Cell ID in a corresponding column to the area identified by the
Cell ID in a corresponding row.
[0052] The O-D matrices, for example, may be expressed as
H.sub.i(t), wherein i=1, 2, 3, . . . , n and represents a serial
number of activity types; t represents time, namely the time with
respect to the O-D matrix, which generally indicates a period of
time, for example 7:00 am-9:00 am, 9:00 am-11:00 am, etc. As to the
duration of the time period, it may be selected as required. The
O-D matrix may be built based on one hour, 2 hours, and 3 hours, or
any suitable period of time as required. The data between 6:00 am
and 8:00 am may be obtained by combining the data in the O-D matrix
between 6:00 am and 7:00 am and the data in the matrix between 7:00
am and 8:00 am.
[0053] Next, various activities of each user may be counted into
the O-D matrix for a corresponding activity type. For example,
based on the analysis result of the mobility data for each user
obtained when identifying different activity types at step 301,
various activities of each user may be counted into the O-D matrix
for the corresponding activity type. Additionally, data analysis
may be performed again for each user at this step so as to
determine a relevant activity type.
[0054] For example, user 1 moves directly from Cell 1 to Cell 2,
and its activity type is "going to work," then in the O-D matrix
corresponding to this activity type pattern and behavior time, the
element in the second row in the first column is plus 1. In such a
way, the O-D matrices H.sub.i(t) for various activity types are
generated gradually. FIG. 5B schematically illustrates a result of
an O-D matrix for, for example, the "office" activity type within a
certain period of time.
[0055] Preferably, corresponding travel information may be formed
with respect to a part of various activity types as identified at
step 301, namely forming travel information only for a concerned
activity type or only for a dominant activity type.
[0056] Besides, travel modes of users can also be identified based
on mobility data, for example, by a private car, by bus, by subway,
by bike, and by foot, etc.
[0057] Identification of moving modes may be implemented in a
plurality of ways. For example, it may be determined based on an
average transient entropy, through a moving speed distribution, or
based on the slope of the moving speed distribution. Further, it
may be determined through associating a cellular phone number with
a public transportation card number.
[0058] In one embodiment according to the present inventive subject
matter, an average transient entropy (speed) of each user in an
activity is calculated. It is known that under the same condition,
a larger average transient entropy corresponds to a greater speed,
while a smaller average transient entropy corresponds to a lower
speed. Because the average moving velocities of different travel
modes are generally different, different travel modes may be
distinguished based on the average transient entropy. However, for
users in different areas, due to various factors such as size of a
cell, a user who is determined to have a larger average transient
entropy based on the mobility data might not have a speed greater
than a user who has a smaller average moving entropy. Thus, it is
possible to cause misjudgment by means of average transient
entropy.
[0059] FIG. 4C also illustrates a diagram of a method of
identifying travel modes of users. Specifically, this figure
illustrates a relationship between a transient entropy and a
probability density based on the mobility data of two users,
wherein the X axis represents a transient entropy, and the Y axis
represents the corresponding probability density. As illustrated in
FIG. 4C, respective data points of the users may be connected to
form corresponding curves, respectively; and then each curve is
fitted with a straight line. A travel mode is determined based on
the slopes of various straight lines. As illustrated in the figure,
a user having a small absolute value of slope corresponds to a user
traveling by private car, while a user having a larger absolute
value of the slope corresponds to a user traveling by a non-private
car. However, this technique has the possibility of
misjudgment.
[0060] FIG. 4D illustrates a diagram of another method of
identifying travel modes of users. This figure specifically
illustrates circumstances in which the approaches for determining,
through the average transient entropy, the slope, and combination
of slope and average transient entropy information are adopted as
determination criteria, respectively.
[0061] As illustrated in FIG. 4D, if the slope is employed alone (a
bold transverse dotted line as illustrated in the figure), then
users whose slopes are above the transversal dotted line should be
users travelling by private cars. However, as illustrated in the
utmost right dotted line rings in the figure, users traveling by
non-private cars are also determined as users traveling by private
cars; similarly, if an average transient entropy (a bold vertical
dotted line as illustrated in the figure) is individually employed
as the determining criteria, then the users at the left side of the
vertical dotted line should be users travelling by non-private
cars. However, based on this criteria, several users traveling by
private cars are also determined as users traveling by non-private
cars (as illustrated in the utmost left dotted-line rings), and
several users traveling by non-private cars are determined as users
traveling by private cars (as illustrated in the middle dotted-line
rings in the figure). Thus, misjudgment exists in individually
employing any of these individual approaches. However, if they are
combined, misjudgment can be significantly eliminated by employing,
for example, the slanting dotted line as the determining criteria
illustrated in the figure.
[0062] FIG. 4E illustrates a diagram of a still further method of
identifying travel modes of users. This figure specifically
illustrates a speed (transient entropy) distribution situation
within standardized time as obtained based on mobility data of
three users. In this figure, the X axis represents standardized
time, while the Y axis represents speed or transient entropy. The
data of the three users are represented by blocks, rings, and
triangular blocks, respectively. As illustrated in the figure, from
the speed distribution within standardized time, it is seen that
the average speed for the user represented by blocks is relatively
high, the highest speed is very high (as high as 3.3), which is a
typical characteristic of travel by car; the speed of the user
represented by rings change greatly, and the highest speed is
relatively higher but does not exceed 2.5, periodically having the
lowest speed of 0, which is therefore a typical characteristic of
travel by bus. Additionally, the average speed of the user
represented by triangular blocks is relatively low, with a highest
speed of 1.5, which is a typical characteristic of pedestrian.
[0063] Various kinds of travel modes may be identified in the above
exemplary manners. The travel mode information may be for example
stored in an additional table as additional information to the O-D
matrix. The travel mode information, for example, may indicate the
numbers or percentages of various travel modes among the population
flow from one cell to another cell. However, it should be noted
that the above embodiments have been illustrated only for exemplary
purposes. The present inventive subject matter is not so limited,
but may employ any other suitable manner to identify a travel
mode.
[0064] The present inventive subject matter can automatically
identify various activity types of people from the mobility data
actually reflecting users' daily activities and establish travel
information with respect to various activity types based on the
mobility data. Therefore, the present inventive subject matter not
only mitigates the lag from from static data acquisition and
inability of static data techniques to adapt to social development
and change, but the present inventive subject matter also provides
more accurate transportation demand data in a more intelligent way.
In addition, the present inventive subject matter provides a
possibility to predict and estimate changes of travel demands in
response to a travel-related event.
[0065] It should be noted that in the above embodiments, activity
situation of a user within one day may be obtained based on the
mobility data. However, the present inventive subject matter is not
so limited, but may obtain activity situation within other periods
of time, for example, one week.
[0066] Additionally, FIG. 6 further illustrates a flowchart of a
method of providing travel information according to another
embodiment of the present inventive subject matter.
[0067] As illustrated in FIG. 6, step 601 and step 602 are similar
to step 301 and step 302 as illustrated in FIG. 3, respectively,
which will not be detailed for the sake of simplification. As
illustrated in FIG. 6, after forming travel information with
respect to various activity types, an overall travel demand may be
further determined at step 603 based on the travel information with
respect to each activity type. This will be described with
reference to FIGS. 7A to 7C, which schematically illustrate three
different kinds of activity types.
[0068] FIG. 7A illustrates a travel situation under the activity
type of for example "going to work," and a corresponding O-D matrix
established for this activity type, for example, may be expressed
as H.sub.1(t); FIG. 7B illustrates a travel situation for the
activity type of for example "taking children to school," and its
corresponding O-D matrix, for example, may be expressed as
H.sub.2(t); and FIG. 7C illustrates the travel situation for the
activity type of for example "taking children to school in the way
of going to work," and its corresponding O-D matrix may be
expressed as H.sub.3(t).
[0069] When it is required to determine the overall demand, various
types of O-D matrixes may be combined to determine the overall
demand. It may be expressed by the following equation:
S = i = 1 n H i ( t ) Equation 2 ##EQU00002##
[0070] In this way, the overall travel demand data may be obtained
through synthesizing the O-D matrix data of various activity
types.
[0071] Further, at step 604, in response to a travel-related event,
the travel information with respect to a travel-related event may
be further adjusted, so as to obtain the adjusted travel
information.
[0072] Upon receipt of the travel-related event, the feature of the
travel-related event may be analyzed to determine the activity type
to be influenced thereby. Next, adjustment may be performed only to
the travel information related to the activity type to be
influenced. For example, for the "adjusted work hours" regulation,
if it is regulated that the working time be changed from 8:00 am to
9:00 am, then merely those users who need not take children to
school might postpone the travel due to this regulation, while to
those users who have to take children to school (assuming that the
school time generally starts from 8:00 am), they generally would
not change their travel. Thus, travel information may be adjusted
towards only the activities of those users who directly go to
work.
[0073] FIG. 7D schematically illustrates a case of an activity type
which is influenced by a travel-related event, which, for example,
may be identified as F(H.sub.3(t)), where F( ) denotes adjustment
performed towards H.sub.3(t).
[0074] Further, it may determine at step 605 the adjusted travel
demand with respect to the travel-related event based on the
adjusted travel information.
[0075] After the above adjustment, travel information with respect
to various activity types may be synthesized to determine the
travel demand estimate with respect to the travel-related event,
wherein the estimate S' may, for example, be expressed by the
following equation:
S ' = F ( H j ( t ) ) + i .noteq. j n H i ( t ) Equation 3
##EQU00003##
[0076] In this way, a more reliable travel demand estimate may be
obtained for various kinds of behavior-related events, thereby
providing data basis on which the measure can be taken and the
decision can be made with respect to the event.
[0077] It should be noted that hereinbefore the present inventive
subject matter has been previously described with respect to
transportation-related applications. However, the present inventive
subject matter may also be applied to any other suitable field. For
example, a plurality of concerned locations within a certain area
may be selected, for example, a shopping area, to obtain the travel
information related to these concerned locations as well as the
population flow condition regarding these locations, thereby
determining whether it is necessary to build a new shopping mall.
Besides, a location of a new shopping mall may be hypothesized,
and, based on this hypothesis, the adjusted travel information is
obtained so as to determine whether the location of this
hypothetical shopping mall is proper.
[0078] FIG. 8 schematically illustrates a systematic architecture
of an application environment of the present inventive subject
matter. As illustrated in FIG. 8, mobility data of users are sent
to the MSC via a base station and stored therein. A travel
information server capable of implementing the present inventive
subject matter obtains the mobility data in the MSC. These mobility
data are analyzed and travel information with respect to various
activity types is formed. A service user, for example, a
transportation department or a transportation planning department,
may obtain the travel information from the travel information
server, thereby forming travel demand data. Besides, the service
user may also input a travel-related event, and the travel
information server may adjust or modify corresponding travel
information in response to the event, and obtain a travel demand
estimate with respect to the travel-related event preferably based
on the adjusted travel information.
[0079] Further, FIG. 9 further illustrates an apparatus for
providing travel information according to an embodiment of the
present inventive subject matter.
[0080] As illustrated in FIG. 9, the apparatus 900 may comprise a
type identification unit 901 and an information formation unit 902,
wherein the type identifying unit 901 is for identifying activity
types of users from mobility data containing time and location
information; and information formation unit 902 is for forming
travel information with respect to various activity types based on
the mobility data.
[0081] In one embodiment according to the present inventive subject
matter, the type identification unit 901 may be configured to
determine daily moving behaviors of the users based on the mobility
data and to determine the purpose of the moving behaviors based on
the nature of a location and/or time for the moving behaviors, so
as to determine the activity types.
[0082] According to another embodiment of the present inventive
subject matter, the information formation unit 902 is configured to
only form the travel information with respect to the activity type
of interest.
[0083] In a further embodiment of the present inventive subject
matter, the travel information may be in a form of O-D matrix, and
the information may comprise origination, destination, population
flow, and travel mode.
[0084] In a still further embodiment of the present inventive
subject matter, the information formation unit 902 further
comprises a matrix construction unit 902-1 for constructing an O-D
matrix for each of activity types; and a data count unit 902-2 for
counting data related to each of activity types of each user into
the O-D matrix for a corresponding activity type.
[0085] In a yet further embodiment of the present inventive subject
matter, the apparatus 900 may further comprise demand determination
unit 903, for determining an overall travel demand based on the
travel information with respect to various activity types.
[0086] In a further embodiment of the present inventive subject
matter, the apparatus 900 may further comprise an information
adjustment unit 904 for, in response to a travel-related event,
adjusting the travel information with respect to an activity type
related to the travel-related event.
[0087] In a still further embodiment of the present inventive
subject matter, the information adjustment unit 904 comprises event
analysis unit 904-1 for performing an analysis on the
travel-related event so as to determine an activity type to be
influenced based on feature of the travel-related event; and
information with respect to the activity type to be influenced.
[0088] In a yet further embodiment of the present inventive subject
matter, the apparatus 900 further comprises estimation
determination unit 905 for determining an estimate on a travel
demand for the travel-related event based on the adjusted travel
information.
[0089] The mobility data may comprise one or more of: call data,
SMS data, webpage browsing data, packet switched data, and location
cell handover data.
[0090] For detailed operations of respective unit in the apparatus
900 for providing travel information, please refer to the specific
descriptions of the methods for providing travel information in
conjunction with FIG. 3 to FIG. 7D and the systematic architecture
of the application environment of the present inventive subject
matter in conjunction with FIG. 8.
[0091] Hereinafter, a computer device in which the present
inventive subject matter can be implemented will be described with
reference to FIG. 10. FIG. 10 shows a structural block diagram of a
computer device capable of implementing the embodiments according
to the present inventive subject matter.
[0092] The computer system as shown in FIG. 10 includes a CPU
(Central Processing Unit) 1001, a RAM (Random Access Memory) 1002,
a ROM (Read Only Memory) 1003, a system bus 1004, a hard disk
controller 1005, a keyboard controller 1006, a serial interface
controller 1007, a parallel interface controller 1008, a display
controller 1009, a hard disk 1010, a keyboard 1011, a serial
peripheral device 1012, a parallel peripheral device 1013 and a
display 1014. Among these components, connected to the system bus
1004 are the CPU 1001, the RAM 1002, the ROM 1003, the hard disk
controller 1005, the keyboard controller 1006, the serial interface
controller 1007, the parallel interface controller 1008 and the
display controller 1009. The hard disk 1010 is connected to the
hard disk controller 1005; the keyboard 1011 is connected to the
keyboard controller 1006; the serial peripheral device 1012 is
connected to the serial interface controller 1007; the parallel
peripheral device 1013 is connected to the parallel interface
controller 1008; and the display 1014 is connected to the display
controller 1009.
[0093] The structural block diagram in FIG. 10 is shown only for
illustration purpose, and is not intended to limit the inventive
subject matter. In some cases, some devices can be added or reduced
as required.
[0094] Further, the embodiments of the present inventive subject
matter can be implemented in software, hardware or the combination
thereof. The hardware part can be implemented by a special logic;
the software part can be stored in a memory and executed by a
proper instruction execution system such as a microprocessor or a
dedicated designed hardware. The normally skilled in the art may
understand that the above method and system may be implemented with
a computer-executable instruction and/or in a processor control
code, for example, such code is provided on a bearer medium such as
a magnetic disk, CD, or DVD-ROM, or a programmable memory such as a
read-only memory (firmware) or a data bearer such as an optical or
electronic signal bearer. The apparatus and its components in the
present embodiments may be implemented by hardware circuitry such
as a very large scale integrated circuit or gate array, a
semiconductor for example logical chip or transistor, or
programmable hardware device for example a field-programmable gate
array, or a programmable logical device, or implemented by software
executed by various kinds of processors, or implemented by
combination of the above hardware circuitry and software.
[0095] Although the present inventive subject matter has been
described with reference to the embodiments of the present
inventive subject matter considered by far, it should be understood
that the inventive subject matter is not limited to the embodiments
disclosed herein. On the contrary, all modifications and equivalent
arrangements that fall within the spirit and range of the appended
claims are intended to be embraced therein. The scope of the
appended claims is accorded with the broadest interpretation to
encompass all such modifications and equivalent structures and
functions.
[0096] As will be appreciated by one skilled in the art, aspects of
the present inventive subject matter may be embodied as a system,
method or computer program product. Accordingly, aspects of the
present inventive subject matter may take the form of an entirely
hardware embodiment, an entirely software embodiment (including
firmware, resident software, micro-code, etc.) or an embodiment
combining software and hardware aspects that may all generally be
referred to herein as a "circuit," "module" or "system."
Furthermore, aspects of the present inventive subject matter may
take the form of a computer program product embodied in one or more
computer readable medium(s) having computer readable program code
embodied thereon.
[0097] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0098] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0099] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0100] Computer program code for carrying out operations for
aspects of the present inventive subject matter may be written in
any combination of one or more programming languages, including an
object oriented programming language such as Java, Smalltalk, C++
or the like and conventional procedural programming languages, such
as the "C" programming language or similar programming languages.
The program code may execute entirely on the user's computer,
partly on the user's computer, as a stand-alone software package,
partly on the user's computer and partly on a remote computer or
entirely on the remote computer or server. In the latter scenario,
the remote computer may be connected to the user's computer through
any type of network, including a local area network (LAN) or a wide
area network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0101] Aspects of the present inventive subject matter are
described with reference to flowchart illustrations and/or block
diagrams of methods, apparatus (systems) and computer program
products according to embodiments of the inventive subject matter.
It will be understood that each block of the flowchart
illustrations and/or block diagrams, and combinations of blocks in
the flowchart illustrations and/or block diagrams, can be
implemented by computer program instructions. These computer
program instructions may be provided to a processor of a general
purpose computer, special purpose computer, or other programmable
data processing apparatus to produce a machine, such that the
instructions, which execute via the processor of the computer or
other programmable data processing apparatus, create means for
implementing the functions/acts specified in the flowchart and/or
block diagram block or blocks.
[0102] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0103] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0104] Plural instances may be provided for components, operations
or structures described herein as a single instance. Finally,
boundaries between various components, operations and data stores
are somewhat arbitrary, and particular operations are illustrated
in the context of specific illustrative configurations. Other
allocations of functionality are envisioned and may fall within the
scope of the inventive subject matter. In general, structures and
functionality presented as separate components in the exemplary
configurations may be implemented as a combined structure or
component. Similarly, structures and functionality presented as a
single component may be implemented as separate components. These
and other variations, modifications, additions, and improvements
may fall within the scope of the inventive subject matter.
* * * * *